Methods and systems for determining when to decommission vehicles from a fleet of autonomous vehicles

ABSTRACT

Example methods and systems for determining when to decommission vehicles from a fleet of autonomous vehicles are described. One method includes receiving information indicating a sequence of coverage requirements for a region over a period of time, and the region is characterized by landmarks and the period of time is divided into time intervals. Landmarks may be defined as a decommissioning site representative of a location at which a given vehicle can be taken out of service, and the method includes determining estimated landmarks that can be reached by one or more vehicles starting from a respective landmark by an end of a respective time interval, and based on the sequence of coverage requirements, determining which vehicles from among the vehicles that can reach the one or more landmarks defined as the decommissioning site to take out of service.

BACKGROUND

Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.

Computing devices such as personal computers, laptop computers, tablet computers, cellular phones, and countless types of Internet-capable devices are increasingly prevalent in numerous aspects of modern life. As such, the demand for data connectivity via the Internet, cellular data networks, and other such networks, is growing. However, there are many areas of the world where data connectivity is still unavailable, or if available, is unreliable and/or costly. Accordingly, additional network infrastructure is desirable.

SUMMARY

In one example, a method is provided that comprises receiving information indicative of a sequence of coverage requirements for a region over a period of time, and the region is characterized by a plurality of landmarks and the period of time is divided into a plurality of time intervals. An individual coverage requirement identifies a desired number of vehicles of a plurality of vehicles for given landmarks at a given time interval. The method also includes defining one or more landmarks as a decommissioning site representative of a location at which a given vehicle can be taken out of service. The method also includes determining, by a processor, for at least one respective landmark of the plurality of landmarks, estimated landmarks of the plurality of landmarks that can be reached by one or more vehicles starting from the respective landmark by an end of a respective time interval. The method further includes based on the sequence of coverage requirements and the estimated landmarks that can be reached by the one or more vehicles, determining, by the processor, which vehicles from among the vehicles that can reach the one or more landmarks defined as the decommissioning site to take out of service.

In another example, a computer readable storage medium having stored therein instructions, that when executed by a computing device, cause the computing device to perform functions is provided. The functions comprise receiving information indicative of a sequence of coverage requirements for a region over a period of time, and the region is characterized by a plurality of landmarks and the period of time is divided into a plurality of time intervals. An individual coverage requirement identifies a desired number of vehicles of a plurality of vehicles for given landmarks at a given time interval. The functions further comprise defining one or more landmarks as a decommissioning site representative of a location at which a given vehicle can be taken out of service. The functions further comprise determining for at least one respective landmark of the plurality of landmarks, estimated landmarks of the plurality of landmarks that can be reached by one or more vehicles starting from the respective landmark by an end of a respective time interval. The functions further comprise based on the sequence of coverage requirements and the estimated landmarks that can be reached by the one or more vehicles, determining which vehicles from among the vehicles that can reach the one or more landmarks defined as the decommissioning site to take out of service.

In still another example, a system is provided that comprises at least one processor, and data storage comprising program instructions executable by the at least one processor to cause the at least one processor to perform functions. The functions comprise receiving information indicative of a sequence of coverage requirements for a region over a period of time, and the region is characterized by a plurality of landmarks and the period of time is divided into a plurality of time intervals. An individual coverage requirement identifies a desired number of vehicles of a plurality of vehicles for given landmarks at a given time interval. The functions also comprise defining one or more landmarks as a decommissioning site representative of a location at which a given vehicle can be taken out of service. The functions also comprise determining for at least one respective landmark of the plurality of landmarks, estimated landmarks of the plurality of landmarks that can be reached by one or more vehicles starting from the respective landmark by an end of a respective time interval. The functions further comprise based on the sequence of coverage requirements and the estimated landmarks that can be reached by the one or more vehicles, determining, by the processor, which vehicles from among the vehicles that can reach the one or more landmarks defined as the decommissioning site to take out of service.

These as well as other aspects, advantages, and alternatives, will become apparent to those of ordinary skill in the art by reading the following detailed description, with reference where appropriate to the accompanying figures.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a block diagram illustrating an example balloon network.

FIG. 2 is a block diagram illustrating an example balloon-network control system.

FIG. 3 shows a high-altitude balloon according to an example embodiment.

FIG. 4 illustrates example trajectories for a balloon that is traveling from a plurality of starting landmarks to a landmark E.

FIG. 5 illustrates an example cost value map where location A is a starting location of a balloon, location B is a desired final location, and example cost values are assigned to cells surrounding location B.

FIG. 6 illustrates estimated destination cells for a balloon starting at cell X, and moving with one of the five wind vectors A₁-A₅.

FIGS. 7A-7B illustrate examples of determining a set of starting landmarks from which a balloon could reach a landmark E.

FIGS. 8A-8B illustrate example graphs of possible routes between a plurality of landmarks in accordance with an example embodiment.

FIG. 9 illustrates an example directed graph in accordance with an example embodiment.

FIG. 10 illustrates an example piecewise linear cost function in accordance with an example embodiment.

FIG. 11 is an example block diagram of a method to determine trajectories for a fleet of balloons, in accordance with at least some embodiments described herein.

DETAILED DESCRIPTION

The following detailed description describes various features and functions of the disclosed systems and methods with reference to the accompanying figures. In the figures, similar symbols identify similar components, unless context dictates otherwise. The illustrative system and method embodiments described herein are not meant to be limiting. It may be readily understood that certain aspects of the disclosed systems and methods can be arranged and combined in a wide variety of different configurations, all of which are contemplated herein.

Examples herein may be configured to provide control of vehicles within a fleet of vehicles. The vehicles may be any number of types of vehicles including, for example, autonomous or semi-autonomous vehicles, such as aerial vehicles, boats, underwater vehicles, satellites, aerostats, etc. for mapping/surveying, sensing, providing connectivity, etc. In other examples, methods described herein may be used to control or dispatch autonomous cars to maintain a well-distributed fleet of autonomous taxis in a city to support rapid dispatch of taxis. In still other examples, vehicles may include vehicles, mobile vehicles, objects, mobile elements, or mobile objects within a fleet of vehicles.

In still further examples, vehicles may be or include vehicles of a data network that includes a plurality of balloons, for example, configured as a mesh network formed by high-altitude balloons deployed in the stratosphere. Since winds in the stratosphere may affect locations of the balloons in a differential manner, each balloon in an example network may be configured to change its horizontal position by adjusting its vertical position (i.e., altitude). For example, by adjusting its altitude, a balloon may be able find winds that will carry the balloon horizontally (e.g., latitudinally and/or longitudinally) to a desired horizontal location. In further examples, balloons may include propellers for horizontal thrust rather than (or in addition to) wind.

Within examples, methods and systems are described with respect to vehicles of a fleet of vehicles being balloons in a fleet of balloons arranged as a data network. However, as mentioned above, methods and systems herein may be applicable to other types of vehicles of a fleet of vehicles, and thus, examples described are not limiting.

In some examples, methods and systems for determining when to decommission vehicles from a fleet of autonomous vehicles are provided. An example method may include receiving information indicative of a sequence of coverage requirements for a region over a period of time. The region may be characterized by a plurality of landmarks and the period of time may be divided into a plurality of time intervals. An individual coverage requirement identifies a desired number of vehicles of a plurality of vehicles for given landmarks at a given time interval. The method includes defining one or more landmarks as a decommissioning site representative of a location at which a given vehicle can be taken out of service. The method also includes determining for at least one (or in some example, for each) respective landmark of the plurality of landmarks, estimated landmarks of the plurality of landmarks that can be reached by one or more vehicles starting from the respective landmark by an end of a respective (or in some examples, by an end of each) time interval. The method further includes based on the sequence of coverage requirements and the estimated landmarks that can be reached by the one or more vehicles, determining which vehicles from among the vehicles that can reach the one or more landmarks defined as the decommissioning site to take out of service.

Thus, example methods can be performed to determine an optimal time and location at which a vehicle can be taken out of service while still operating the fleet of vehicles to satisfy coverage requirements.

FIG. 1 is a block diagram illustrating an example balloon network 100. As shown, the balloon network 100 includes balloons 102A to 102F, which are configured to communicate with one another via free-space optical links 104. Configured as such, balloons 102A to 102F may collectively function as a mesh network for packet-data communications. Further, at least some of balloons 102A and 102B may be configured for RF communications with ground-based stations 106 via respective RF links 108, or may be configured for RF communications between balloons. Yet further, some balloons, such as balloon 102F, may be configured to communicate via optical link 110 with ground-based station 112.

In an exemplary embodiment, balloons 102A to 102F are high-altitude balloons, which are deployed in the stratosphere. At moderate latitudes, the stratosphere includes altitudes between approximately 10 kilometers (km) and 50 km altitude above the surface. At the poles, the stratosphere starts at an altitude of approximately 8 km. In an exemplary embodiment, high-altitude balloons may be generally configured to operate in an altitude range within the stratosphere that has relatively low wind-speeds (e.g., at 17.5 km-20 km altitude, a global mean wind speed may be about 30-35 miles per hour (mph) during portions of the year with higher wind speeds up to about 200 mph being possible as well).

More specifically, in a high-altitude-balloon network, balloons 102A to 102F may generally be configured to operate at altitudes between 18 km and 25 km (although other altitudes are possible). At such altitudes, the winds may vary with latitude and by season, and the variations can be modeled in a reasonably accurate manner. Additionally, altitudes above 18 km are typically above the maximum flight level designated for commercial air traffic. Therefore, interference with commercial flights is not a concern when balloons are deployed between 18 km and 25 km.

To transmit data to another balloon, a given balloon 102A to 102F may be configured to transmit an optical signal via an optical link 104. In an exemplary embodiment, a given balloon 102A to 102F may use one or more high-power light-emitting diodes (LEDs) to transmit an optical signal. Alternatively, some or all of balloons 102A to 102F may include laser systems for free-space optical communications over optical links 104. Other types of free-space optical communication are possible. Further, in order to receive an optical signal from another balloon via an optical link 104, a given balloon 102A to 102F may include one or more optical receivers.

In a further aspect, balloons 102A to 102F may utilize one or more of various different RF air-interface protocols for communication with ground-based stations 106 via respective RF links 108. For instance, some or all of balloons 102A to 102F may be configured to communicate with ground-based station 106 using protocols described in IEEE 802.11 (including any of the IEEE 802.11 revisions), various cellular protocols such as GSM, CDMA, UMTS, EV-DO, WiMAX, and/or LTE, and/or one or more propriety protocols developed for balloon-ground RF communication, among other possibilities.

In a further aspect in balloon network 100, balloon 102F is configured as a downlink balloon to provide a high-capacity air-ground link. Like other balloons in an exemplary network, a downlink balloon 102F may be operable for optical communication with other balloons via optical links 104. However, a downlink balloon 102F may also be configured for free-space optical communication with a ground-based station 112 via an optical link 110. Optical link 110 may therefore serve as a high-capacity link (as compared to an RF link 108) between the balloon network 100 and ground-based station 112.

Note that in some implementations, a downlink balloon 102F may additionally be operable for RF communication with ground-based station 106. In other cases, a downlink balloon 102F may only use an optical link for balloon-to-ground communications. Further, while the arrangement shown in FIG. 1 includes just one downlink balloon 102F, an exemplary balloon network can also include multiple downlink balloons. On the other hand, a balloon network can also be implemented without any downlink balloons.

In other implementations, a downlink balloon may be equipped with a specialized, high-bandwidth RF communication system for balloon-to-ground communications, instead of, or in addition to, a free-space optical communication system. The high-bandwidth RF communication system may take the form of an ultra-wideband system, which may provide an RF link with substantially the same capacity as one of the optical links 104.

Ground-based stations, such as ground-based station 106 and/or 112, may include components such as transceivers, transmitters, and/or receivers for communication via RF links and/or optical links with a balloon network. Further, a ground-based station may use various air-interface protocols in order to communicate with a balloon 102A to 102F over an RF link. As such, a ground-based station 106 may be configured as an access point via which various devices can connect to balloon network 100.

In a further aspect, some or all balloons 102A to 102F could be configured to establish a communication link with space-based satellites in addition to, or as an alternative to, a ground-based communication link. In some embodiments, a balloon may communicate with a satellite via an optical link.

Further, some ground-based stations, such as ground-based station 106, may be configured as gateways between balloon network 100 and one or more other networks. Such a ground-based station 106 may thus serve as an interface between the balloon network and the Internet, a cellular service provider's network, and/or other types of networks.

As noted, balloons 102A to 102F may collectively function as a mesh network. More specifically, since balloons 102A to 102F may communicate with one another using free-space optical links, the balloons may collectively function as a free-space optical mesh network.

In a mesh-network configuration, each balloon 102A to 102F may function as a node of the mesh network, which is operable to receive data directed to the balloon and to route data to other balloons. As such, data may be routed from a source balloon to a destination balloon by determining an appropriate sequence of optical links between the source balloon and the destination balloon. These optical links may be collectively referred to as a “lightpath” for the connection between the source and destination balloons. Further, each of the optical links may be referred to as a “hop” on the lightpath.

To operate as a mesh network, balloons 102A to 102F may employ various routing techniques and self-healing algorithms. In some embodiments, a balloon network 100 may employ adaptive or dynamic routing, where a lightpath between a source and destination balloon is determined and set-up when the connection is needed, and released at a later time. Further, when adaptive routing is used, the lightpath may be determined dynamically depending upon the current state, past state, and/or predicted state of the balloon network.

In addition, the network topology may change as the balloons 102A to 102F move relative to one another and/or relative to the ground. Accordingly, an exemplary balloon network 100 may apply a mesh protocol to update the state of the network as the topology of the network changes. For example, to address the mobility of the balloons 102A to 102F, balloon network 100 may employ and/or adapt various techniques that are employed in mobile ad hoc networks (MANETs). Other examples are possible as well.

In some implementations, a balloon network 100 may be configured as a transparent mesh network. For instance, the balloons may include components for physical switching that is entirely optical, without any electrical components involved in the physical routing of optical signals. Thus, in a transparent configuration with optical switching, signals travel through a multi-hop lightpath that is entirely optical.

In other implementations, the balloon network 100 may implement a free-space optical mesh network that is opaque. In an opaque configuration, some or all balloons 102A to 102F may implement optical-electrical-optical (OEO) switching. For example, some or all balloons may include optical cross-connects (OXCs) for OEO conversion of optical signals. Other opaque configurations are also possible.

In a further aspect, balloons in an exemplary balloon network 100 may implement wavelength division multiplexing (WDM), which may help to increase link capacity. When WDM is implemented with transparent switching, physical lightpaths through the balloon network may be subject to the “wavelength continuity constraint.” More specifically, because the switching in a transparent network is entirely optical, the same wavelength can be assigned for all optical links on a given lightpath.

An opaque configuration, on the other hand, may avoid the wavelength continuity constraint. In particular, balloons in an opaque balloon network may include the OEO switching systems operable for wavelength conversion. As a result, balloons can convert the wavelength of an optical signal at each hop along a lightpath.

Further, various routing algorithms may be employed in an opaque configuration. For example, to determine a primary lightpath and/or one or more diverse backup lightpaths for a given connection, exemplary balloons may apply or consider shortest-path routing techniques such as Dijkstra's algorithm and k-shortest path, and/or edge and node-diverse or disjoint routing such as Suurballe's algorithm, among others. Additionally or alternatively, techniques for maintaining a particular quality of service (QoS) may be employed when determining a lightpath. Other techniques are also possible.

In some examples, a balloon network 100 may have a non-uniform topology. For instance, exemplary embodiments may involve topologies where balloons are distributed more or less densely in certain areas, for various reasons. As an example, to help meet the higher bandwidth demands that are typical in urban areas, balloons may be clustered more densely over urban areas. For similar reasons, the distribution of balloons may be denser over land than over large bodies of water. Many other examples of non-uniform topologies are possible.

FIG. 2 is a block diagram illustrating an example balloon-network control system. In particular, FIG. 2 shows a distributed control system, which includes a central control system 200 and a number of regional control-systems 202A to 202C. Such a control system may be configured to coordinate certain functionality for balloon network 204, and as such, may be configured to control and/or coordinate certain functions for balloons 206A to 206I.

In the illustrated embodiment, central control system 200 may be configured to communicate with balloons 206A to 206I via a number of regional control systems 202A to 202C. These regional control systems 202A to 202C may be configured to receive communications and/or aggregate data from balloons in the respective geographic areas that the balloons cover, and to relay the communications and/or data to central control system 200. Further, regional control systems 202A to 202C may be configured to route communications from central control system 200 to the balloons in their respective geographic areas. For instance, as shown in FIG. 2, regional control system 202A may relay communications and/or data between balloons 206A to 206C and central control system 200, regional control system 202B may relay communications and/or data between balloons 206D to 206F and central control system 200, and regional control system 202C may relay communications and/or data between balloons 206G to 206I and central control system 200.

In order to facilitate communications between the central control system 200 and balloons 206A to 206I, certain balloons may be configured as downlink balloons, which are operable to communicate with regional control systems 202A to 202C. Accordingly, each regional control system 202A to 202C may be configured to communicate with the downlink balloon or balloons in the respective geographic area it covers. For example, in the illustrated embodiment, balloons 204A, 204D, and 204H are configured as downlink balloons. As such, regional control systems 202A to 202C may respectively communicate with balloons 204A, 204D, and 204H via optical links 206, 208, and 210, respectively.

In a centralized control arrangement, such as that shown in FIG. 2, the central control system 200 (and possibly regional control systems 202A to 202C as well) may coordinate certain mesh-networking functions for balloon network 204. For example, balloons 206A to 206I may send the central control system 200 certain state information, which the central control system 200 may utilize to determine the state of balloon network 204. The state information from a given balloon may include location data, optical-link information (e.g., the identity of other balloons with which the balloon has established an optical link, the bandwidth of the link, wavelength usage and/or availability on a link, etc.), wind data collected by the balloon, and/or other types of information. Accordingly, the central control system 200 may aggregate state information from some or all of the balloons 206A to 206I in order to determine an overall state of the network.

The overall state of the network may then be used to coordinate and/or facilitate certain mesh-networking functions such as determining lightpaths for connections. For example, the central control system 200 may determine a current topology based on the aggregate state information from some or all of the balloons 206A to 206I. The topology may provide a picture of the current optical links that are available in the balloon network and/or the wavelength availability on the links. This topology may then be sent to some or all of the balloons so that a routing technique may be employed to select appropriate lightpaths (and possibly backup lightpaths) for communications through the balloon network 204.

In a further aspect, the central control system 200 (and possibly regional control systems 202A to 202C as well) may also coordinate functions for balloon network 204. For example, the central control system 200 may input state information that is received from balloons 206A to 206I to an energy function, which may effectively compare the current topology of the network to a desired topology, and provide a vector indicating a direction of movement (if any) for each balloon, such that the balloons can move towards the desired topology. Further, the central control system 200 may use altitudinal wind data to determine respective altitude adjustments that may be initiated to achieve the movement towards the desired topology. The central control system 200 may provide and/or support other station-keeping functions as well.

In some embodiments, control and coordination of a balloon network may be shared by a ground-based control system and a balloon network to varying degrees, depending upon the implementation. In fact, in some embodiments, there may be no ground-based control systems. In such an embodiment, all network control and coordination functions may be implemented by the balloon network itself. For example, certain balloons may be configured to provide the same or similar functions as central control system 200 and/or regional control systems 202A to 202C. Other examples are also possible.

Furthermore, control and/or coordination of a balloon network may be de-centralized. For example, each balloon may relay state information to, and receive state information from, some or all nearby balloons. Further, each balloon may relay state information that it receives from a nearby balloon to some or all nearby balloons. When all balloons do so, each balloon may be able to individually determine the state of the network. Alternatively, certain balloons may be designated to aggregate state information for a given portion of the network. These balloons may then coordinate with one another to determine the overall state of the network.

Various types of balloon systems may be incorporated in an exemplary balloon network. As noted above, an exemplary embodiment may utilize high-altitude balloons, which typically operate in an altitude range between 18 km and 25 km. FIG. 3 shows a high-altitude balloon 300, according to an exemplary embodiment. As shown, the balloon 300 includes an envelope 302, a skirt 304, a payload 306, and a cut-down system 308 that is attached between the balloon 302 and payload 306.

The envelope 302 and skirt 304 may take various forms, for instance, the envelope 302 and/or skirt 304 may be made of metalized Mylar or BoPet. Alternatively or additionally, some or all of the envelope 302 and/or skirt 304 may be constructed from a highly-flexible latex material or a rubber material such as chloroprene. Further, the shape and size of the envelope 302 and skirt 304 may vary depending upon the particular implementation. Additionally, the envelope 302 may be filled with various different types of gases, such as helium and/or hydrogen.

The payload 306 of balloon 300 may include a processor 312 and on-board data storage, such as memory 314. The memory 314 may take the form of or include a non-transitory computer-readable medium. The non-transitory computer-readable medium may have instructions stored thereon, which can be accessed and executed by the processor 312 in order to carry out the balloon functions described herein.

The payload 306 of balloon 300 may also include various other types of equipment and systems to provide a number of different functions. For example, payload 306 may include optical communication system 316, which may transmit optical signals via an ultra-bright LED system 320, and which may receive optical signals via an optical-communication receiver 322 (e.g., a photo-diode receiver system). Further, payload 306 may include an RF communication system 318, which may transmit and/or receive RF communications via an antenna system 340. The payload 306 may further include a positioning system 324 that can include a GPS or other location-based sensors.

The payload 306 may also include a power supply 326 to supply power to the various components of balloon 300. The power supply 326 may include or take the form of a rechargeable battery. In addition, the balloon 300 may include a solar power generation system 327. The solar power generation system 327 may include solar panels and could be used to generate power that charges and/or is distributed by the power supply 326.

Further, payload 306 may include various types of other systems 328 and sensors 334. For example, payload 306 may include one or more video and/or still cameras, a GPS system, various motion sensors (e.g., accelerometers, gyroscopes, and/or compasses), and/or various sensors for capturing environmental data. Further, some or all of the components within payload 306 may be implemented in a radiosonde, which may be operable to measure, e.g., pressure, altitude, geographical position (latitude and longitude), temperature, relative humidity, and/or wind speed and/or direction, among other information.

As noted, balloon 300 includes an ultra-bright LED system 320 for free-space optical communication with other balloons. As such, optical communication system 316 may be configured to transmit a free-space optical signal by modulating the ultra-bright LED system 320. The optical communication system 316 may be implemented with mechanical systems and/or with hardware, firmware, and/or software.

In a further aspect, balloon 300 may be configured for altitude control. For instance, balloon 300 may include a variable buoyancy system, which is configured to change the altitude of the balloon 300 by adjusting the volume and/or density of the gas in the balloon 300. A variable buoyancy system may take various forms, and may generally be any system that can change the volume and/or density of gas in envelope 302.

In an exemplary embodiment, a variable buoyancy system may include a bladder 310 that is located inside of envelope 302. The bladder 310 could be an elastic chamber configured to hold liquid and/or gas. Alternatively, the bladder 310 need not be inside the envelope 302. For instance, the bladder 310 could be a rigid bladder that could be pressurized well beyond neutral pressure. The buoyancy of the balloon 300 may therefore be adjusted by changing the density and/or volume of the gas in bladder 310. To change the density in bladder 310, balloon 300 may be configured with systems and/or mechanisms for heating and/or cooling the gas in bladder 310. Further, to change the volume, balloon 300 may include pumps or other features for adding gas to and/or removing gas from bladder 310. Additionally or alternatively, to change the volume of bladder 310, balloon 300 may include release valves or other features that are controllable to allow gas to escape from bladder 310. Multiple bladders 310 could be implemented within the scope of this disclosure. For instance, multiple bladders could be used to improve balloon stability.

In an example embodiment, the envelope 302 could be filled with helium, hydrogen or other lighter-than-air material. The envelope 302 could thus have an associated upward buoyancy force. In such an embodiment, air in the bladder 310 could be considered a ballast tank that may have an associated downward ballast force. In another example embodiment, the amount of air in the bladder 310 could be changed by pumping air (e.g., with an air compressor) into and out of the bladder 310. By adjusting the amount of air in the bladder 310, the ballast force may be controlled. In some embodiments, the ballast force may be used, in part, to counteract the buoyancy force and/or to provide altitude stability.

In other embodiments, the envelope 302 could be substantially rigid and include an enclosed volume. Air could be evacuated from envelope 302 while the enclosed volume is substantially maintained. In other words, at least a partial vacuum could be created and maintained within the enclosed volume. Thus, the envelope 302 and the enclosed volume could become lighter than air and provide a buoyancy force. In yet other embodiments, air or another material could be controllably introduced into the partial vacuum of the enclosed volume in an effort to adjust the overall buoyancy force and/or to provide altitude control.

In another embodiment, a portion of the envelope 302 could be a first color (e.g., black) and/or a first material from the rest of envelope 302, which may have a second color (e.g., white) and/or a second material. For instance, the first color and/or first material could be configured to absorb a relatively larger amount of solar energy than the second color and/or second material. Thus, rotating the balloon such that the first material is facing the sun may act to heat the envelope 302 as well as the gas inside the envelope 302. In this way, the buoyancy force of the envelope 302 may increase. By rotating the balloon such that the second material is facing the sun, the temperature of gas inside the envelope 302 may decrease. Accordingly, the buoyancy force may decrease. In this manner, the buoyancy force of the balloon could be adjusted by changing the temperature/volume of gas inside the envelope 302 using solar energy. In such embodiments, it is possible that a bladder 310 may not be a necessary element of balloon 300. Thus, various contemplated embodiments, altitude control of balloon 300 could be achieved, at least in part, by adjusting the rotation of the balloon with respect to the sun.

Further, the balloon 300 may include a navigation system (not shown). The navigation system may implement station-keeping functions to maintain position within and/or move to a position in accordance with a desired topology. In particular, the navigation system may use altitudinal wind data to determine altitudinal adjustments that result in the wind carrying the balloon in a desired direction and/or to a desired location. The altitude-control system may then make adjustments to the density of the balloon chamber in order to effectuate the determined altitudinal adjustments and cause the balloon to move laterally to the desired direction and/or to the desired location.

Alternatively, the altitudinal adjustments may be computed by a ground-based control system and communicated to the high-altitude balloon. As another alternative, the altitudinal adjustments may be computed by a ground-based or satellite-based control system and communicated to the high-altitude balloon. Furthermore, in some embodiments, specific balloons in a heterogeneous balloon network may be configured to compute altitudinal adjustments for other balloons and transmit the adjustment commands to those other balloons.

As shown, the balloon 300 also includes a cut-down system 308. The cut-down system 308 may be activated to separate the payload 306 from the rest of balloon 300. This functionality may be utilized anytime the payload needs to be accessed on the ground, such as when it is time to remove balloon 300 from a balloon network, when maintenance is due on systems within payload 306, and/or when power supply 326 needs to be recharged or replaced.

In an exemplary embodiment, the cut-down system 308 may include a connector, such as a balloon cord, connecting the payload 306 to the envelope 302 and a means for severing the connector (e.g., a shearing mechanism or an explosive bolt). In an example embodiment, the balloon cord, which may be nylon, is wrapped with a nichrome wire. A current could be passed through the nichrome wire to heat it and melt the cord, cutting the payload 306 away from the envelope 302. Other types of cut-down systems and/or variations on the illustrated cut-down system 308 are possible as well.

In an alternative arrangement, a balloon may not include a cut-down system. In such an arrangement, the navigation system may be operable to navigate the balloon to a landing location, in the event the balloon needs to be removed from the network and/or accessed on the ground. Further, it is possible that a balloon may be self-sustaining, such that it theoretically does not need to be accessed on the ground. In yet other embodiments, balloons may be serviced in-flight by specific service balloons or another type of service aerostat or service aircraft.

As described, movement and locations of balloons can vary since winds in the stratosphere may affect the locations of the balloons in a differential manner. A balloon in an example network may be configured to change its horizontal position by adjusting its vertical position (i.e., altitude). For example, by adjusting its altitude, a balloon may be able to find winds that will carry the balloon horizontally (e.g., latitudinally and/or longitudinally) to a desired horizontal location. Wind speed and/or direction may vary with altitude, and since current wind velocities as well as weather forecasts are available, in principle, a balloon may be directed to a location by identifying an altitude at which a wind direction takes a balloon along a desired trajectory. However, a balloon without other forms of propulsion may be constrained to follow the wind and there may not be a single altitude with winds taking the balloon along the desired trajectory. In other examples, balloons may include propellers for horizontal thrust rather than (or in addition to) using an altitude control strategy. In addition, to control a fleet of balloons, movement of the balloons should occur from one location above the surface of the Earth to another in a predictable manner.

In some examples, determining a fleetplan for a fleet of balloons may require staging balloons of the fleet into places ahead of time such that the balloons will be able to arrive at a landmark on time. For instance, a balloon may need to follow a trajectory that does not contribute usefully to goals during a first phase and a second phase such that the balloon can reach a desired landmark at the end of a third phase. This kind of staging may be useful when goal landmarks are spread non-uniformly throughout a fleet's coverage area. For example, there may be large regions (e.g., relative to a balloon's maximum range during a phase) requiring few or no balloons, but which may need to be traversed in order to satisfy a goal number of balloons for other regions. Such situations arise naturally, for example, when goal distributions are proportional to population density: there are a limited number of people in the oceans, few people on many parts of land, and many people in major cities.

In one case, a system may be configured to receive (or determine) a starting location of each balloon of a fleet of balloons and receive a sequence of coverage requirements for the region for a planning time period. As an example, the sequence of coverage requirements for the time period may specify a goal number of balloons for each of a plurality of landmarks within the region at various times T during the time period. The system may also be configured to divide the time period into phases based on distinct values of T in the sequence of coverage requirements: a first phase may be defined from a starting time of the time period to an earliest T; a second phase may be defined between the end of the first phase and a second earliest T, etc. Additionally, a set of landmarks may be associated with the start and end of each phase: the start of the first phase may use the initial location of each of the balloons as landmarks; the end of the first phase and the start of the second phase may share the set of landmarks associated with the earliest T, etc.

Based on the received information, the system may be further configured to determine trajectories that satisfy the sequence of coverage requirements for the region. To determine the trajectories, the system may initially establish possible routes for balloons within each phase. For each landmark at the start of a phase and for each landmark at the end of the phase, the system may determine how close to the end-of-phase landmark that a balloon starting from the start-of-phase landmark could get by traveling during the phase.

FIG. 4 illustrates example trajectories for a balloon that is traveling from a plurality of starting landmarks to a landmark E. Specifically, FIG. 4 illustrates trajectories from each of landmark A, B, C, D, and F to an ending landmark E. As shown in FIG. 4, a region 400 has been divided into a plurality of subregions, and the landmarks A-L have been established at the center of each subregion. Note that in some examples coverage requirements may specify a goal number of balloons to be located at one or more locations within the region 400, and the goal number of balloons may be added to the nearest landmark to determine the goal number of balloons for each landmark. For example, if an individual coverage requirement indicates that five balloons are desired at location Loc1 and seven balloons are desired at location Loc2, the desired number of balloons for landmark A may be determined to be twelve.

Additionally, although the landmarks A-L have been distributed uniformly throughout the region 400, the example is not meant to be limiting. In other instances, landmarks may be non-uniformly distributed within the region 400. For example, if a region covers the entire Earth, one or more oceans or countries may not include any landmarks.

The example trajectories may be determined based on estimated or predicted wind conditions at one or more altitudes. In the example of FIG. 4, the wind conditions are assumed to generally include winds flowing from left-to-right with additional variation in the vertical direction from time T=0 to T=1. Therefore, it is assumed that a balloon could not flow from landmarks G-L to landmark E from time T=0 to T=1, and for convenience, only trajectories from landmarks A, B, C, D, and F are shown in FIG. 4.

In one example, a backward planner may be used to determine a measure of how close to a target location that a balloon could reach if the balloon was starting at another location within a region. For example, given a target time and target location as well as an initial time, the backward planner may be configured to determine, for a grid of starting locations within a region, a measure of how close to the target location a balloon could get, if the balloon was flying from initial time to target time.

The backward planner may consider a problem of minimizing a cost function associated with a balloon path. As an example, the cost function may be a measure of how close a balloon gets to a target location by following a path. FIG. 5 illustrates an example cost value map where location A is a starting location of a balloon, location B is a desired final location, and example cost values are assigned to cells surrounding location B. For a time period from T=0 to T=N, the time period can be discretized in order to consider a situation of the balloon at times T=0, 1, 2, . . . , N. The region can also be discretized by dividing the region into a finite number of cells. For instance, FIG. 5 illustrates a region that has been divided into a number of cells (e.g., a 10×7 grid of cells). Although the region is represented as a rectangular grid, the example is not meant to be limiting.

At time T=N (e.g., a final time of a planning phase), there is no planning to do since the balloon will be at a final location at this time, and a cost value can be assigned to each cell based on a final location of the balloon. For time T=N, cost values may be systematically assigned such that a cell in which the desired location resides has a low cost and cells further away may be assigned a higher cost value (e.g., the farther away from the desired location, the higher the cost value). Thus, as shown, a cell in which the desired location B resides can be assigned a cost value of zero, and cost values for surrounding cells can be assigned higher cost values based on a distance away from the desired location cell. Cost values for all cells can be populated in this manner by increasing the cost value for a given cell based on the distance from the desired location.

Following, cost value maps of all cells may be generated for each time step T=0, 1, 2, . . . , N−1. To construct the cost value maps, a map may be generated for time T=N−1, and maps for previous times may be generated working backwards, such as next generating a map for time T=N−2, followed by T=N−3, and so on.

After generating the map at time T=N, to generate a map for a next previous time step, possible altitudes and corresponding possible locations that can be reached over the time interval from T=N−1 to T=N for a balloon present in every cell at time T=N−1 can be estimated. For each cell and for one or more altitudes, an estimation of where the balloon will arrive by starting at that cell and flying at that altitude during the time interval can be determined. For example, weather predictions at time T=N−1 may indicate that, for a given cell, different wind conditions exist for different altitudes. FIG. 6 illustrates an area divided into cells (e.g., a 10 by 7 grid of cells), and for a given cell labeled X, example wind vectors A₁-A₅ that are determined based on wind conditions at five different altitude levels.

In some examples, wind vectors associated with different altitudes may be the same for each cell. In other examples, wind vectors associated with different altitudes may vary based on the location of the cell. For each cell, an estimation of where a balloon would travel to (e.g., destination cell) at the end of the time period if the balloon started at the cell and moved with a given wind vector is determined. To do so, a direction and magnitude (e.g., speed) of the estimated wind vector can be used to estimate a trajectory and distance that the balloon travels, and to determine estimated destination cells (e.g., A_(1(EST))-A_(5(EST))). FIG. 6 illustrates estimated destination cells for a balloon starting at cell X, and moving with one of the five wind vectors A₁-A₅. Example simulations can be performed to simulate movement of the balloons due to the estimated wind directions and speed, for example.

In some examples, further estimations may be made by a balloon traveling at a first altitude for a portion of a time interval and then changing to a second altitude for a remainder of the time interval. It could be possible that by traveling at any given number of combinations of altitudes, the winds may be able to carry the balloon to a desired location over the time interval.

As shown in the example in FIG. 6, wind vector A₃ results in the balloon reaching a closest proximity to the desired final location B at time T=N. Cost values for the balloon traveling along the wind vectors A₁-A₅ may be determined based on the stored cost values at T=N for the destination cells A_(1(EST))-A_(5(EST)). For example, the wind vector A₃ may be assigned a value of zero, and cost values for a remainder of the wind vectors may be assigned higher cost values for causing the balloon to travel to a location further away from the desired destination (e.g., such as a A₄ having cost of one, A₅ having cost of four, A₂ having cost of two, and A₁ having cost of three).

An altitude that results in a smallest cost value can be determined, and the altitude and cost value can be stored for the cell X for time T=N−1. Thus, in the example described, for cell X at time T=N−1, a balloon in cell X that has a desired destination location B at time T=N can be instructed to travel at altitude A₃. The same determinations may be performed for each cell of the area.

The process above may be repeated to determine cost value maps for time interval T=N−2 based on predicted wind conditions for T=N−2 to T=N−1 and the stored cost values for T=N−1. Further, the process may be repeated for time intervals T=N−3, T=N−4, and so on until T=0. In some examples, each cell of a given map has estimates of directions that are based on estimates of directions as determined for a subsequent time interval. This enables planning for the balloon to take a given path at a first time interval so that the balloon will be at a location needed during the next time interval to take appropriate action, and so on.

Referring back to the example illustrated by FIG. 4, the backward planner may be able to determine, for each starting landmark A-L, a minimum distance from landmark E that a balloon could get, if the balloon was flying from T=0 to T=1. For example, a cost value for each of landmarks A-L may be proportional to a distance such that a given cost value can be converted to a given three-dimensional distance. The backward planner may also be able to determine, for each starting landmark A-L, which altitude a balloon should fly at T=0 in order to get as close as possible to the landmark E by T=1.

The backward planner is provided as one example mechanism for establishing possible routes between landmarks during a phase. However, other mechanisms that can determine, for a particular staring location/time and an ending location/time, how close to the ending location a balloon can get in the time period allotted, based on predicted wind velocities at one or more altitudes may also be utilized.

In some examples, filtering heuristics may also be applied to further refine the possible routes between landmarks during a phase. FIGS. 7A-7B illustrate examples of determining a set of starting landmarks from which a balloon could reach a landmark E.

In one example, thresholding may be used to impose a notion of how close to a target landmark that a balloon needs to be able to get in order to be considered to be capable of reaching the landmark. For example, a set of starting landmarks may be selected based on a comparison between the minimum distances associated with paths from landmarks A-D and F and a threshold distance. Based on the trajectories shown in FIG. 7A, balloons traveling from landmarks A, B, and F could get within a threshold distance 702 of the landmark E. Therefore, the landmarks A, B, and F may be retained as a set of starting landmarks from which a balloon could reach the landmark E, while the landmarks D and C may be removed.

In another example, a set of starting landmarks may be selected based on a confidence of reaching a target landmark from a starting landmark and/or a cost value associated with flying to the target landmark from a starting landmark. As shown in FIG. 7B, each path from starting landmarks A-D and F includes an associated probability, as indicated by a percentage on a scale of 1 to 100. The probability may be determined based on the variability of one or more wind conditions (e.g., wind direction and/or speed). In one example, based on the associated probabilities, landmarks A and B and C may be selected as a set of possible starting landmarks because their associated probabilities are greater than a confidence threshold (e.g., 85%).

Additionally, as shown in FIG. 7B, each path from starting landmarks A-D and F includes an associated cost, as indicated by a dollar amount. In one example, the cost may be associated with a cost in power that is necessary to adjust the altitude of a balloon to reach one or more altitudes during the path. Accordingly, a set of starting landmarks may be selected based on a comparison between the cost values associated with each starting landmark and a cost threshold. For instance, starting landmark B may be selected as the only possible starting landmark because the path associated with landmark B has the only associated cost that is below 3$.

In still another example, a set of starting landmarks may be determined from among the possible starting landmarks by retaining the top N number of starting landmarks (e.g., sorted to prefer minimum distances from within the target landmark that a balloon could get). It is contemplated that one or any combination of the described filtering heuristics may be used to determine a set of starting landmarks from which a target landmark may be reached.

FIG. 8A illustrates an example graph 800 of possible routes between a plurality of landmarks in accordance with an example embodiment. Continuing with the example from FIG. 4, landmarks A-F are provided. Each edge between two landmarks represents a trajectory a balloon could follow to get from a landmark at the beginning of a phase to a landmark at the end of the phase. For instance, a balloon at landmark A at time T=0 may be able to get to landmark D or E by time T=1. Additionally, a balloon at landmark A at time T=1 may be able to get to landmark D or E by time T=2. Note that although the edges for phase 1 are the same as the edges for phase 2 in FIG. 8A, in other examples, edges may vary among phases.

Once the graph is constructed, an optimization method may be used to choose how many balloons flow across each connection in the graph (i.e., from each starting landmark to each ending landmark) during each phase. The optimization method may be configured to obey one or more consistency constraints. For example, a number of balloons leaving a landmark in phase P may be set to be the same as the number of balloons that arrived at that landmark at the end of phase P−1. As another example, a number of balloons at any landmark or traveling along any edge cannot be negative.

In some examples, balloons or other vehicles within a fleet may have a finite lifetime, and methods are provided to determine when and where to decommission vehicles from the fleet. Example methods may consider and balance factors of decommissioning vehicles at or near a decommissioning site, or in a location at which the vehicle may be recovered, as well as choosing a practical time for decommissioning (e.g., when staff needed to support a decommissioning is on duty). Other factors that may be considered include determining what goals will go unsatisfied due to fewer vehicles in operation, a cost of decommissioning, determining if other vehicles need to be launched or staged to fill in for the decommissioned vehicle, what decommissioning stations are reachable, and how the fleet plan can include moving to-be-decommissioned vehicles to the decommissioning sites.

Fleet planning can thus also consider making determinations of when and where to decommission vehicles out of the operating fleet of vehicles, which can be limited due to a number of possible decommission locations and/or possible decommission times. Fleet planning considers what goals can be satisfied based on decommissioning a vehicle at a given site and time, and how a decommissioned vehicle can be coordinated with the fleet to maximize usage of the fleet and maintenance of the vehicles.

To determine when to decommission vehicles, in addition to landmarks considered at an end of each phase, an additional landmark can be included for each possible decommission site, and may be considered a “decommission site landmark”. No landmark may be added to a start-of-phase landmark sets since a decommission site is a location where vehicles go to, not a location where vehicles come from. Thus, decommission site landmarks are included to generate a graph of possible paths of vehicles that can be decommissioned out of the fleet.

A decommissioning site may be a location vehicles travel to so as to be taken out of the fleet, and possibly serviced, refurbished, or recycled, for example.

FIG. 8B illustrates another example graph 802 of possible routes between a plurality of landmarks in accordance with an example embodiment. The graph 802 in FIG. 8B includes all the landmarks in the graph 800 in FIG. 8A with the addition of landmarks B′, C′, and G, which are each defined or designated as decommission site landmarks. Landmarks B′ and C′ may each be the same location as landmarks B and C, and thus, any balloon that can reach landmarks B and C can also reach landmarks B′ and C′ during the phases, as shown in the graph 802. In other examples, it may not be the case that a balloon can reach the same landmarks as those already operating in the fleet during the phase due to a time necessary to decommission the balloon out of the fleet or to take the balloon offline. The graph 802 shows an additional decommission site landmark labeled G, at which a balloon may be decommission, for example.

For the graph 802, decommission site landmarks are only included at an end of a phase since no balloon would leave a decommission site landmark.

Each decommission site can accept some number of balloons over a time period or over a planning phase. Every balloon that flows to a decommission site landmark across any path may be considered eligible for decommission if the decommission site is not full for that time period.

For fleet planning, constraints for a decommission site may be used as before, however, any consistency constraint on a number of balloons leaving decommission site landmarks based on a number of balloons starting the phase may be removed so there are no paths leaving any decommission site.

There may be constraints on a total number of balloons arriving at a decommission site due to available capacity. In some examples, however, there may be no practical limit on a number of balloons that a decommission site can decommission during any time period, and thus, no constraint on the total number of balloons arriving at the decommissioning site is required. If there is not a practical limit on the total number of balloons that a decommission site can decommission during the planning session, there may be a limit on a number of balloons that can be decommissioned during a time period (e.g., due to personnel needed to manage a decommission, or due to limited physical resources required for decommission, etc.). Each decommission site can have a constraint limiting the number of vehicles arriving at the site during each time period.

Other constraints may be placed on determining when to decommission balloons. For example, if a renewable resource (such as storage space) is required at the site to decommission balloons, and this resource can only be renewed at a particular rate, then the amount of resource can be taken into account. A decommissioning site might start a first phase with a fixed supply (S₁) of the resource, and an amount of resource available at a start of a subsequent phase is (S₂=S₁−D+A), where D is an amount or resource consumed by balloons decommissioned during that phase, and A is an amount of resource that could be replenished during the phase. By constraining the resource supply variables to be non-negative, the fleet planner can determine that balloons are decommissioned when the appropriate resources are available.

In addition, other constraints may be considered to represent cost of not decommissioning a vehicle that should be decommissioned. For example, the fleet planner may estimate a likelihood of failure of a balloon at each point in time based on factors such as balloon age (e.g., year of manufacture), time of operation (e.g., time since last launch), or other balloon health diagnostic information. Balloon health degradation may be estimated from phase to phase as well, either a priori or based on specific paths the balloon travels (e.g., wear and tear, non-renewable resource consumption, etc.). By modeling a probability that a balloon will fail if not decommissioned in a timely manner, a penalty term can be added as a constraint.

In other examples, other constraints may be considered such as a financial cost to decommission a balloon.

Thus, within examples, fleet planning can consider a number of balloons to be decommissioned or taken out of service over time when performing the planning. There may be limits on a number of balloons that can be decommissioned per hour, or balloons may only be decommissioned during certain times of day. All of these factors may be considered during fleet planning.

If the sequence of coverage requirements is treated as strict requirements, the problem of determining trajectories for the fleet may be considered a constraint satisfaction problem, where optimization means solving the constraint graph subject to the constraint that each goal is satisfied. If it is allowable to sometimes leave some goals unsatisfied, the problem may be considered an optimization problem: find the configuration of the fleet that, for example, minimizes the number of unsatisfied goals, or some other measure of the goals' dissatisfaction.

Such a problem can be solved using a variety of methods, including as a linear program, as a minimum-cost-flow problem, via gradient descent, via stochastic optimization, or using other existing or future developed methods.

For instance, the problem may be solved as a linear program. In one case, the goal for the linear program is to take a time-varying description of how many balloons are desired at each landmark (“the goal distribution”) that may be known from the sequence of coverage requirements, the initial location of each balloon, and a summary of which goal landmarks are reachable from which other landmarks at a given time, along with a measure of how accurately a balloon can be expected to reach a goal landmark, and determine a fleet-wide policy that maximizes a likelihood of satisfying the goal distribution. Note that the goal distribution may not be concerned with determining where any particular balloon goes, but rather that the correct numbers of balloons are in the correct areas at each phase.

To solve the problem as a linear program, constants, variables, and constraints may be defined.

The constants may include:

-   -   startingAtOrigin[origin]=the initial distribution; the number of         balloons at each location at the beginning of a planning period,         and can include balloons in supply at launch site locations     -   requiredAtGoal[phase, goal]=the goal distribution; how many         balloons desired at each goal at the end of each phase     -   score[phase, origin, goal]=accuracy estimate for the path from         origin to goal during phase.

The variables may include:

-   -   A[phase, origin, goal]=how many balloons move from origin to         goal during phase; defined if it is possible to reach the goal         from the origin given the wind at that phase.     -   atGoal[phase, goal]=the number of balloons at goal at the end of         phase     -   deficit[phase, goal]=the number of balloons by which the goal         distribution was under-satisfied at goal at the of phase, that         is: max(0, requiredAtGoal−atGoal).

The constraints may include:

-   -   Non-negative flow         -   ∀origin,∀goal,∀phase: 0≦A[phase, origin, goal]     -   Conservation on the origin side         -   ∀origin: Σ_(goal)A[0, origin, goal]=startingAtOrigin[origin]         -   ∀origin,∀phase>0: Σ_(goal)A[phase, origin, goal]=atGoal             [phase−1, origin]     -   Conservation on the goal side         -   ∀goal,∀phase: atGoal[phase, goal]=Σ_(origin)A[phase, origin,             goal]     -   Non-negative deficits         -   ∀goal,∀phase: 0≦deficit[phase, goal]         -   ∀goal,∀phase: requiredAtGoal[phase, goal]≦atGoal[phase,             goal]+deficit[phase, goal]

Based on the constants, variables, and constraints defined above, a linear program solver may be used to minimize the example objective function shown below: α·c _(error)(Σ_(phase)Σ_(goal)deficit[phase,goal])+(1−α)(Σ_(phase)Σ_(origin)Σ_(goal)score[phase,origin,goal]A[phase,origin,goal])

The objective function penalizes fleet plans that put less than the minimum number of balloons at some landmark; for each balloon fewer than desired, the penalty goes up. The optimal plan, therefore, is the plan that minimizes the objective function. An additional term can also be included that minimizes a measure of how close to a target landmark that each balloon can get. For instance, the additional term seeks to minimize the sum of the score constant for each path traversed. The additional term causes the linear program solver to prefer accurate trajectories and penalize trajectories that do not reliably deliver the balloon directly to a goal landmark.

In the example objective function, c_(error) is a weight parameter and a may be another parameter that is used to adjust the relative importance of minimizing the number of missed goals versus preferring accurate trajectories. For instance, increasing α·c_(error) may cause the linear program solver to prefer fleet trajectories that satisfy a goal distribution better, at the cost of being willing to use less accurate flight paths. Also, increasing (1−α) may cause the linear program solver to prefer fleet plans that use accurate/reliable flight paths, at the cost of being willing to leave more of a goal distribution unsatisfied.

In further examples, the score constant may be a measure of a confidence associated with whether a balloon traveling from origin could reach goal during phase. For instance, the measure of confidence may be a probability that is determined based on the variability of one or more wind conditions (e.g., wind direction and/or speed). In addition, the configuration of the linear program could further be modified such that the number of balloons atGoal is an expected number of balloons that is determined based on the probabilities of each balloon reaching the goal landmark. For example, if a balloon has a 75% chance of reaching a goal landmark, the balloons contribution to atGoal may be 0.75 balloons instead of 1 balloon.

In another variant, the score constant may be replaced or augmented with a measure of cost associated with a path from origin to goal during phase. For instance, the cost may be a cost-in-dollars, cost-in-power, or other cost measure. The linear program solver may then be configured to minimize total cost in the determined trajectories.

In still further examples, another term may be included in the objective function that is a sum across all decommission sites of the number of balloons decommission multiplied by a cost of decommissioning each vehicle (i.e., c_(decommission)). Thus, minimizing the objective function encourages the system to not decommission a balloon, but allows balloons to be decommissioned if the rewards justify the cost. The optimal plan, therefore, is the plan that minimizes the objective function. The objective function may penalize fleet plans that unnecessarily decommission balloons. The objection function may thus take the form shown below: α·c _(error)(Σ_(phase)Σ_(goal)deficit[phase,goal])+(1−α)(Σ_(phase)Σ_(origin)Σ_(goal)score[phase,origin,goal]A[phase,origin,goal])+(Σ_(phase)Σ_(origin)Σ_(goal)Decommission[phase,origin,goal])*c _(decommission) where Decommission[phase, origin, goal] is how many balloons were decommissioned from origin to goal during phase.

In some examples, a justification for decommissioning a balloon despite the cost to do so may include doing so near or at a decommission site so that the balloon can be easily recovered versus having the balloon fail at an unknown location. Selecting when to decommission a balloon using a cut-down or guided descent may be a better alternative than allowing the balloon to operate until failure. Decommissioning a balloon prior to absolute failure or full use of a life of a balloon can be offset by a risk of losing a balloon. In other examples, risks of losing a balloon may in fact be justified to satisfy certain coverage requirements, and in such examples, a choice to decommission a balloon may be delayed.

In general, fleet planning may be performed with a preference of not to decommission a balloon. Balloons in a fleet of balloons may be described by metrics indicating how much life is left on the balloon, for example. In some examples, a benefit may be derived by cutting down the balloon early to recover the balloon easily. Thus, those balloons that may reach a decommission site and that are near an end of life of operation may be cut-down at that time.

In still further examples, another term may be included in the objective function to represent a cost of not decommissioning a vehicle that should be decommissioned. For example, the system might estimate a likelihood of failure of each balloon at each point in time, based on factors such as balloon age, time since last launch, or other balloon health diagnostic information. The system may opt to model balloon health degradation from phase to phase either a priori or based on specific paths the balloon travels (e.g., wear and tear, non-renewable resource consumption, etc.). By modeling a probability that a balloon will fail if not decommissioned in a timely way, the objective function can include a penalty term modeling an expected cost of vehicle failures.

Thus, within examples, the linear program (e.g., objective function, constraints, variables, and constants) may be modified to add a term incorporating an “age” dimension to the terms A, startingAtOrigin, and atGoal, which may take into account one or both of how old the balloon is (based on year of manufacture) and how long the balloon has been in operation (based on time since last launch), or other factors as well. That is, the constraints of the linear program may take the form of:

-   -   Non-negative flow         -   ∀origin,∀goal,∀phase,∀age: 0≦A[phase, origin, goal, age]     -   Conservation on the origin side         -   ∀origin,∀age: Σ_(goal)A[0, origin, goal,             age]=startingAtOrigin[origin, age]         -   ∀origin,∀phase>0,∀age: Σ_(goal)A[phase, origin, goal,             age]=atGoal[phase−1, origin, age]     -   Conservation on the goal side         -   ∀goal,∀phase,∀age: atGoal[phase, goal,             age]=Σ_(origin)A[phase, origin, goal, age]     -   Non-negative deficits         -   ∀goal,∀phase: 0≦deficit[phase, goal]         -   ∀goal,∀phase: requiredAtGoal[phase, goal]≦deficit[phase,             goal]+Σ_(age) atGoal[phase, goal, age]

Within examples, aging of the fleet can be incorporated by replacing conservation on the origin side of the objective function with the following terms:

-   -   ∀origin,∀age: Σ_(goal)A[0, origin, goal,         age]=startingAtOrigin[origin, age—1]     -   ∀origin,∀phase>0,∀age: Σ_(goal)A[phase, origin, goal,         age]=atGoal[phase−1, origin, age−1]

For each phase, there may be one age “bin” equivalent to one phase duration, e.g., if age bins discretize age into 6-hour increments and if phases are 6 hours apart. Alternatively, if age bins are measured in day or week increments, then the above age adjustments may occur on the day or week boundaries (e.g., every 4 or 28 phases, if phases are 6 hours long).

In yet further examples, the objective function may be modified to penalize having old balloons in operation in the fleet (i.e., to account for a possible risk of flying a balloon that may be likely to fail). For example, other terms may be added to the objective function, such as the following:

-   -   Σ_(age)(c_(fail) _(_)         _(penalty)[age]*Σ_(phase)Σ_(origin)Σ_(goal)A[phase, origin,         goal, age])

The linear program can minimize the objective function to solve for A[phase, origin, goal], how many balloons should move from origin to goal during phase, which conceptually may be shown as traveling along paths illustrated in FIGS. 8A-8B. As one example, the value of A at A[1, A, D] will indicate how many balloons should flow from landmark A to landmark D during phase 1, the value at A[1, A, E] will indicate how many balloons should flow from landmark A to landmark E during phase 1, etc.

The optimization problem may also be solved as a minimum-cost-flow problem. For instance, the linear program described above may be reduced to a minimum-cost-flow problem. A minimum-cost-flow problem seeks to find the least costly way of sending a certain amount of flow through a flow network. The flow network is often represented as a directed graph with one or more source nodes and one or more sink nodes. Every edge in the network then specifies a maximum capacity of flow for the edge and a positive cost per unit of flow.

FIG. 9 illustrates an example directed graph 900 in accordance with an example embodiment. The directed graph 900 may be formed in a manner similar to the graph 800 of FIG. 8A and 802 of FIG. 8B. For convenience, only two phases, phase P and phase P+1, are shown. During “Phase P flow” and “Phase P+1 flow”, each edge represents a path between two landmarks. These edges may be considered to all have unlimited capacity. The cost for these edges is used to penalize trajectories that do not reliably deliver a balloon directly to a goal landmark. Similar to the linear program described above, each edge has a cost of: (1−α)·score[phase, origin, goal].

Each initial balloon location is defined as a source node. Therefore, the number of balloons entering the directed graph 900 corresponds to the number of balloons starting at that location. The directed graph 900 also includes a sink node, where the number of balloons exiting the directed graph 900 equals the total number of balloons in the fleet. Decommission site landmarks may further be defined as additional nodes, and one is shown in the directed graph 900 for a first phase P (additional decommission site landmarks may be included during the first phase or during subsequent phases, however, only one is shown for simplicity). The edge to the decommission site may have a cost of: c_(decommission). Additionally, the edge to the decommission site may have a limited or unlimited capacity. This allows the balloons to end up in any configuration at the end of the final phase, and then all get accounted for as exiting exactly once.

Thus, within examples, up to a certain number of balloons may be decommissioned during a phase, and an edge to the existing sink node from the decommission site may have a maximum number and cost, and an edge from the initial position node to the decommission site node may have an unlimited number and cost of c_(decommission). The additional term, MaxDecommission may be added to the linear program as well as a constraint, such as for example:

-   -   ∀phase,∀origin: Σ_(decommision) _(site)         ,Σ_(age)Decommission[phase, origin, decommissionSite,         age]≦MaxDecommission         MaxDecommission is the maximum number of balloons a single site         can decommission during a single phase. If this is site or time         dependent (i.e., some decommission sites may have different         capacities, or some sites may only be fully operational at         certain times of the day) then MaxDecommission can also be         subscripted as MaxDecommission[phase, goal or         decommission_site].

The minimum-cost-flow problem class can be configured to consider the physical constraints defined in the linear program as givens or initial inputs. In particular, non-negative flow and conservation of balloons may be required features for minimum-cost-flow problems.

To include consideration of deficits in the minimum-cost-flow problem, the directed graph 900 includes a space to apply the deficit costs. Deficits are defined at the nodes in between phase flows by splitting each landmark node 902 into two nodes. Two edges are then included between the two nodes. Specifically, FIG. 9 includes a “Phase P deficit checks” column and “Phase P+1 deficit checks” column, where each landmark node 902 has been split into two nodes that are connected by two edges.

The deficits can be defined according to a piecewise linear cost function. FIG. 10 illustrates an example piecewise linear cost function 1000 in accordance with an example embodiment. The piecewise linear cost function 1000 allows the minimum-cost-flow problem to account for unlimited capacity at the landmark node 902. Because the cost of the top edge has a lower cost than the bottom edge, balloons will prefer to go across the top edge, up to the capacity limit of required[phase, goal] (which may be equal to requiredAtGoal as defined above). If more than a required number of balloons travel across the landmark node 902, the additional balloons in excess of the desired number of balloons for the landmark node 902 will incur a cost of α*c_(error) while traveling across the infinite capacity bottom edge. Therefore, a solution to the minimum-cost-flow problem will seek to avoid having more than the desired number of balloons travel to each landmark node 902.

FIG. 11 is an example block diagram of a method 1100 to determine trajectories for a fleet of balloons, in accordance with at least some embodiments described herein. Method 1100 shown in FIG. 11 presents an embodiment of a method that, for example, could be used with the balloon networks 100 and 204, for example, and may be performed by a device, a server, a control station, a balloon, or a combination of any components of the balloon networks 100 and 204. Method 1100 may include one or more operations, functions, or actions as illustrated by one or more of blocks 1102-1108. Although the blocks are illustrated in a sequential order, these blocks may in some instances be performed in parallel, and/or in a different order than those described herein. Also, the various blocks may be combined into fewer blocks, divided into additional blocks, and/or removed based upon the desired implementation.

In addition, for the method 1100 and other processes and methods disclosed herein, the flowchart shows functionality and operation of one possible implementation of present embodiments. In this regard, each block may represent a module, a segment, or a portion of program code, which includes one or more instructions executable by a processor for implementing specific logical functions or steps in the process. The program code may be stored on any type of computer readable medium, for example, such as a storage device including a disk or hard drive. The computer readable medium may include a non-transitory computer readable medium, for example, such as computer-readable media that stores data for short periods of time like register memory, processor cache and Random Access Memory (RAM). The computer readable medium may also include non-transitory media, such as secondary or persistent long term storage, like read only memory (ROM), optical or magnetic disks, compact-disc read only memory (CD-ROM), for example. The computer readable media may also be any other volatile or non-volatile storage systems. The computer readable medium may be considered a computer readable storage medium, a tangible storage device, or other article of manufacture, for example.

In addition, for the method 1100 and other processes and methods disclosed herein, each block in FIG. 11 may represent circuitry that is wired to perform the specific logical functions in the process.

At block 1102, the method 1100 includes receiving information indicative of a sequence of coverage requirements for a region over a period of time. The region may be characterized by a plurality of landmarks and the period of time is divided into a plurality of time intervals, and an individual coverage requirement identifies a desired number of vehicles of a plurality of vehicles for given landmarks at a given time interval. In one example, each coverage requirement may be specified in the form of a goal distribution. For instance, the goal distribution may be determined based on population densities for different parts of the region or short-term coverage demands for one or more locations within the region. Note that in some examples the number of vehicles may be a minimum number of vehicles that is desired for each of the plurality of landmarks, and in some instances, it may be acceptable to have more than the minimum number of vehicles at each landmark. Additionally, in some instances, the minimum number of vehicles may be zero for one or more landmarks and/or one or more phases.

At block 1104, the method 1100 includes defining one or more landmarks as a decommissioning site representative of a location at which a given vehicle can be taken out of service. For example, any landmark that has facilities for decommissioning a vehicle can be defined as a decommission site landmark. In an example in which vehicles of the plurality of vehicles include a balloon within a data network that is operable to provide data communication via optical or radio-frequency (RF) links, the decommission site is representative of the landmark at which the balloon can be recovered due to being cut-down from the atmosphere.

At block 1106, the method 1100 includes, determining, for at least one respective landmark of the plurality of landmarks, estimated landmarks of the plurality of landmarks that can be reached by one or more vehicles starting from the respective landmark by an end of a respective time interval. For instance, a backward planner may be used to determine, for each landmark, a minimum distance from each other landmark that a balloon could get, if the balloon was following one or more predicted winds during the phase.

Additionally, filtering heuristics may be used to determine a set of starting landmarks from which each respective landmark may be reached. For example, based on a comparison of the minimum distance from the respective landmark that a balloon starting from each additional landmark could get and a threshold distance, the set of starting landmarks may be determined. As another example, for each phase and each respective landmark, a confidence measure that is indicative of whether a balloon starting from each additional landmark could reach the respective landmark may be determined. The determined confidence measures may then be compared to a confidence threshold to determine the set of starting landmarks. In still another example, a cost value associated with a balloon starting from each additional landmark and traveling to the respective landmark may be determined and compared to a cost threshold to select the set of starting landmarks. Other filtering heuristics are also possible.

In one example, estimations of landmarks that can be reached are based on predicted winds that propel the balloons. The winds may be determined based on wind data received from one or more balloons of the fleet. In other examples, the wind data may be determined based on wind data received from the National Oceanic and Atmospheric Administration (NOAA).

At block 1108, the method 1100 includes, based on the sequence of coverage requirements and the estimated landmarks that can be reached by the one or more vehicles, determining which vehicles from among the vehicles that can reach the one or more landmarks defined as the decommissioning site to take out of service. For instance, a linear objective function or a minimum-cost-flow process may be solved to determine which respective landmark that each balloon should travel to, and a determination can be made of whether given individual coverage requirements have the desired number of balloons for given landmarks at the given time interval. Based on a number of balloons available and operating in the fleet, and based on the number that can reach decommission site landmarks, balloons may be taken out of service while still leaving a number of balloons in operation so as to satisfy the coverage requirements.

Thus, in some examples, the method 1100 may be performed to determine which vehicles can reach decommission sites, and of those, determine which if any to cut down. In other examples, additional or alternative methods may be performed to determine which vehicles of operating vehicles need to be cut-down (e.g., due to low fuel, required maintenance, etc.) and of those, to determine where and when to do so.

In some examples, the method 1100 may further including determining when to decommission one or more of the plurality of vehicles based on whether the one or more of the plurality of vehicles can reach the one or more landmarks defined as the decommissioning site by the end of the one or more time intervals. Thus, a group of vehicles can be identified as vehicles that are candidates for decommissioning based on being able to reach a decommissioning site.

In further examples, the method may include selecting, from among the vehicles that can reach the one or more landmarks defined as the decommissioning site, one or more vehicles for decommission based on a likelihood of failure of the one or more vehicles by the end of the one or more time intervals. The likelihood of failure may be determined based on many factors including an age of the vehicle, a time of operation of the vehicle, an amount of fuel remaining in the vehicle, etc.

In other examples, where to decommission vehicles may be based on factors at the decommission sites, such as the available capacity at the decommissioning site, an available man-power to perform decommissions, etc.

In further examples, the method 1100 may include determining a fleet plan for the plurality of time intervals based on a number of vehicles of the plurality of vehicles in operation. The fleet plan indicates landmarks of the plurality of landmarks for vehicles of the plurality of vehicles to travel to during each of the one or more time intervals. Based on a surplus of vehicles being available to satisfy the fleet plan, some vehicles that can reach the one or more landmarks defined as the decommissioning site can be selected to be taken out of service. Further, based on a surplus, vehicles may be taken out of service ahead of a scheduled decommission. For example, although a given vehicle may have additional life remaining, a fleet plan may indicate to decommission the vehicle now rather than push the vehicle to an end of life operation due to a surplus of available vehicles to meet demand. In this manner, the vehicle may be refurbished and reinstalled into the fleet.

In some examples, for many types of vehicles, a decommission site may be reached at some time in a middle of a phase. Thus, it may be beneficial to consider a range of decommission times during the phase, and to retain a minimum value across all such times. Storing the decommission time leading to the minimum value may allow a resulting fleet plan to include the decommission time within the phase for decommissioned vehicles. Thus, a decommission time at which to take a given vehicle out of service from the plurality of operating vehicles may be determined, and the decommission time may be a time after a beginning of a respective time interval or during a time interval. In some examples, when determining where a balloon can go from leaving a landmark, the start time of the phase is considered. However, for a decommission site, a decommission of a balloon can be performed at any time when the balloon is at the decommission site. For many types of balloons (or other vehicles in a fleet), delaying the decommission or performing the decommission ahead of schedule might allow some balloons to be decommissioned that could not be taken out of service otherwise due to a change in wind conditions, for example. Thus, a range of decommission times can be considered during the phase, and a minimum value across all such times of how close to a desired ending location that the balloon achieve can be retained. Various decommission times may be determined based on wind data during the decommission times and landmarks that can be reached by following the predicted winds, for example.

In some examples, the method 1100 may further include providing an instruction to each balloon to travel to the determined respective landmark during each phase or to initiate a cut-down or guided descent operation. Thus, a set of instructions can be provided that indicates a travel plan for the balloon. The travel plan may indicate specific altitudes to fly at for various locations within the region. In other examples, an instruction can be sent to the balloon to indicate to change an altitude, and the instruction may be sent when the altitude change is needed.

In some examples, the method 1100 or portions of the method 1100 may be performed by a ground-based station (e.g., the ground-based station 112 in FIG. 1) or a central system (e.g., the central control system 200 in FIG. 2), and instructions can be provided to individual balloons. In other examples, the method 1100 or portions of the method 1100 may be perform by the balloons themselves, or by processors or computing devices residing on the balloon, for example. The balloons may receive any necessary information for performing the method 1100 from a server or ground-base station or from other balloons. In further examples, the method 1100 may be performed by a combination of the balloons and by ground-based stations, and each may be in communication so as to perform functions of the method 1100.

It should be understood that arrangements described herein are for purposes of example only. As such, those skilled in the art will appreciate that other arrangements and other elements (e.g. machines, interfaces, functions, orders, and groupings of functions, etc.) can be used instead, and some elements may be omitted altogether according to the desired results. Further, many of the elements that are described are functional entities that may be implemented as discrete or distributed components or in conjunction with other components, in any suitable combination and location.

While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope being indicated by the following claims, along with the full scope of equivalents to which such claims are entitled. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting. 

What is claimed is:
 1. A method comprising: receiving information indicative of a sequence of coverage requirements for a data network for a region over a period of time, wherein the region is characterized by a plurality of landmarks and the period of time is divided into a plurality of time intervals, and wherein an individual coverage requirement identifies a desired number of vehicles of a plurality of vehicles for given landmarks at a given time interval, wherein vehicles of the plurality of vehicles include a balloon within the data network that is operable to provide data communication via optical or radio-frequency (RF) links for the data network; defining one or more landmarks as a decommissioning site representative of a location at which a given vehicle can be taken out of service; determining, by a processor, for at least one respective landmark of the plurality of landmarks, estimated landmarks of the plurality of landmarks that can be reached by one or more vehicles starting from the respective landmark by an end of a respective time interval; making, by the processor, a determination of whether given individual coverage requirements have the desired number of vehicles of the plurality of vehicles for given landmarks at the given time interval so as to satisfy the given individual coverage requirements for the data network based on changing wind conditions, in which the desired number of vehicles of the plurality of vehicles provide data communications via optical or radio-frequency (RF) links for the data network to operate at the given landmarks of the region over the period of time; based on the sequence of coverage requirements being satisfied and the estimated landmarks that can be reached by the one or more vehicles, selecting, by the processor, from among the vehicles that can reach the one or more landmarks defined as the decommissioning site one or more vehicles for decommission and taking out of service from the data network; and causing the one or more vehicles that are determined to be taken out of service to be taken offline from the data network and to travel to the decommissioning site for cut-down or guided descent resulting in removal from the data network.
 2. The method of claim 1, further comprising determining when to decommission one or more of the plurality of vehicles based on whether the one or more of the plurality of vehicles can reach the one or more landmarks defined as the decommissioning site by an end of a given time interval.
 3. The method of claim 1, further comprising selecting, from among the vehicles that can reach the one or more landmarks defined as the decommissioning site, one or more vehicles for decommission based on a likelihood of failure of the one or more vehicles by an end of a given time interval.
 4. The method of claim 1, further comprising selecting, from among the vehicles that can reach the one or more landmarks defined as the decommissioning site, one or more vehicles for decommission based on an age of the one or more vehicles.
 5. The method of claim 1, further comprising selecting, from among the vehicles that can reach the one or more landmarks defined as the decommissioning site, one or more vehicles for decommission based on a time of operation of the one or more vehicles in a fleet of the plurality of vehicles.
 6. The method of claim 1, further comprising: for a given time interval of the period of time, determining given vehicles of the plurality of vehicles that can reach the one or more landmarks defined as the decommissioning site at some time during the plurality of time intervals; determining to decommission one or more of the given vehicles at a time interval prior to the given time interval.
 7. The method of claim 1, further comprising: determining whether the decommissioning site has available capacity at each of the plurality of time intervals; and determining to decommission at least some of the vehicles based on the available capacity at the decommissioning site.
 8. The method of claim 1, further comprising determining a fleet plan for the plurality of time intervals based on a number of vehicles of the plurality of vehicles in operation, the fleet plan indicating landmarks of the plurality of landmarks for vehicles of the plurality of vehicles to travel to during each of the plurality of time intervals.
 9. The method of claim 8, further comprising based on a surplus of vehicles being available to satisfy the fleet plan, determining which vehicles from among the vehicles that can reach the one or more landmarks defined as the decommissioning site to take out of service.
 10. The method of claim 1, further comprising for one or more vehicles to be taken out of service, determining a given decommissioning site and corresponding time interval at which to take the one or more vehicles out of service so as to schedule a decommission of the one or more vehicles.
 11. The method of claim 10, further comprising based on a surplus of vehicles being available to satisfy the sequence of coverage requirements, determining which vehicles from among the vehicles that can reach the one or more landmarks defined as the decommissioning site to take out of service ahead of a scheduled decommission.
 12. A non-transitory computer readable storage medium having stored therein instructions, that when executed by a computing device, cause the computing device to perform functions comprising: receiving information indicative of a sequence of coverage requirements for a data network for a region over a period of time, wherein the region is characterized by a plurality of landmarks and the period of time is divided into a plurality of time intervals, and wherein an individual coverage requirement identifies a desired number of vehicles of a plurality of vehicles for given landmarks at a given time interval, wherein vehicles of the plurality of vehicles include a balloon within the data network that is operable to provide data communication via optical or radio-frequency (RF) links for the data network; defining one or more landmarks as a decommissioning site representative of a location at which a given vehicle can be taken out of service; determining for at least one respective landmark of the plurality of landmarks, estimated landmarks of the plurality of landmarks that can be reached by one or more vehicles starting from the respective landmark by an end of a respective time interval; making a determination of whether given individual coverage requirements have the desired number of vehicles of the plurality of vehicles for given landmarks at the given time interval so as to satisfy the given individual coverage requirements for the data network based on changing wind conditions, in which the desired number of vehicles of the plurality of vehicles provide data communications via optical or radio-frequency (RF) links for the data network to operate at the given landmarks of the region over the period of time; based on the sequence of coverage requirements being satisfied and the estimated landmarks that can be reached by the one or more vehicles, selecting from among the vehicles that can reach the one or more landmarks defined as the decommissioning site one or more vehicles for decommission and taking out of service from the data network; and causing the one or more vehicles that are determined to be taken out of service to be taken offline from the data network and to travel to the decommissioning site for cut-down or guided descent resulting in removal from the data network.
 13. The non-transitory computer readable storage medium of claim 12, wherein the functions further comprise selecting, from among the vehicles that can reach the one or more landmarks defined as the decommissioning site, one or more vehicles for decommission based on one or more of a likelihood of failure of the one or more vehicles by an end of a given time interval, an age of the one or more vehicles, and a time of operation of the one or more vehicles in a fleet of the plurality of vehicles.
 14. The non-transitory computer readable storage medium of claim 12, wherein the functions further comprise: for one or more vehicles to be taken out of service, determining a given decommissioning site and corresponding time interval at which to take the one or more vehicles out of service so as to schedule a decommission of the one or more vehicles; and based on a surplus of vehicles being available to satisfy the sequence of coverage requirements, determining which vehicles from among the vehicles that can reach the one or more landmarks defined as the decommissioning site to take out of service ahead of a scheduled decommission.
 15. A system, comprising: at least one processor; and data storage comprising program instructions executable by the at least one processor to cause the at least one processor to perform functions comprising: receiving information indicative of a sequence of coverage requirements for a data network for a region over a period of time, wherein the region is characterized by a plurality of landmarks and the period of time is divided into a plurality of time intervals, and wherein an individual coverage requirement identifies a desired number of vehicles of a plurality of vehicles for given landmarks at a given time interval, wherein vehicles of the plurality of vehicles include a balloon within the data network that is operable to provide data communication via optical or radio-frequency (RF) links for the data network; defining one or more landmarks as a decommissioning site representative of a location at which a given vehicle can be taken out of service; determining for at least one respective landmark of the plurality of landmarks, estimated landmarks of the plurality of landmarks that can be reached by one or more vehicles starting from the respective landmark by an end of a respective time interval; making a determination of whether given individual coverage requirements have the desired number of vehicles of the plurality of vehicles for given landmarks at the given time interval so as to satisfy the given individual coverage requirements for the data network based on changing wind conditions, in which the desired number of vehicles of the plurality of vehicles provide data communications via optical or radio-frequency (RF) links for the data network to operate at the given landmarks of the region over the period of time; based on the sequence of coverage requirements being satisfied and the estimated landmarks that can be reached by the one or more vehicles, selecting, by the processor, from among the vehicles that can reach the one or more landmarks defined as the decommissioning site one or more vehicles for decommission and taking out of service from the data network; and causing the one or more vehicles that are determined to be taken out of service to be taken offline from the data network and to travel to the decommissioning site for cut-down or guided descent resulting in removal from the data network.
 16. The system of claim 15, wherein the functions further comprise: determining a fleet plan for the plurality of time intervals based on a number of vehicles of the plurality of vehicles in operation, the fleet plan indicating landmarks of the plurality of landmarks for vehicles of the plurality of vehicles to travel to during each of the plurality of time intervals; and based on a surplus of vehicles being available to satisfy the fleet plan, determining which vehicles from among the vehicles that can reach the one or more landmarks defined as the decommissioning site to take out of service. 